{
 "cells": [
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "NOTEBOOK NOT FINISHED YET. CUDA IMPLEMENTATION needs more work.\n",
    "Define a wrapper function for timing runs."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 37,
   "metadata": {},
   "outputs": [],
   "source": [
    "import cudf\n",
    "import numpy as np\n",
    "from cuml.datasets import make_blobs\n",
    "from cuml.neighbors import NearestNeighbors as cuNearestNeighbors\n",
    "from sklearn.neighbors import NearestNeighbors as skNearestNeighbors\n",
    "import argparse, numpy as np, pandas as pd, timeit, os, subprocess\n",
    "import matplotlib.cm as cm\n",
    "import matplotlib.pyplot as plt\n",
    "\n",
    "def load_LAS(las_fname, dtype='float32'):\n",
    "    \"\"\"\n",
    "    Load LAS or LAZ file (only coordinates) and return pc_xyz and xy vectors. Converts float64 to float32 by default, unless you set dtype='float64'\n",
    "    \"\"\"\n",
    "    from laspy.file import File\n",
    "\n",
    "    inFile = File(las_fname, mode='r')\n",
    "    pc_pc_xyz = np.vstack((inFile.get_x()*inFile.header.scale[0]+inFile.header.offset[0], inFile.get_y()*inFile.header.scale[1]+inFile.header.offset[1], inFile.get_z()*inFile.header.scale[2]+inFile.header.offset[2])).transpose()\n",
    "\n",
    "    #setting datatype to float32 to save memory.\n",
    "    if dtype == 'float32':\n",
    "        pc_pc_xyz = pc_pc_xyz.astype('float32')\n",
    "    return pc_pc_xyz\n",
    "\n",
    "def wrapper(func, *args, **kwargs):\n",
    "    def wrapped():\n",
    "        return func(*args, **kwargs)\n",
    "    return wrapped\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Loading input file: test_data/Pozo_WestCanada_clg.laz... loaded 3,348,668 points\n"
     ]
    }
   ],
   "source": [
    "#Load in data\n",
    "lasfname = 'test_data/Pozo_WestCanada_clg.laz'\n",
    "print('Loading input file: %s... '%lasfname, end='', flush=True)\n",
    "pc_xyz = load_LAS(lasfname, dtype='float32')\n",
    "mean_x = np.nanmean(pc_xyz[:,0])\n",
    "mean_y = np.nanmean(pc_xyz[:,1])\n",
    "mean_z = np.nanmean(pc_xyz[:,2])\n",
    "#subtracting mean value to center around 0 - UTM coordinates tend to be large!\n",
    "pc_xyz[:,0] = pc_xyz[:,0] - mean_x\n",
    "pc_xyz[:,1] = pc_xyz[:,1] - mean_y\n",
    "pc_xyz[:,2] = pc_xyz[:,2] - mean_z\n",
    "print('loaded %s points'%\"{:,}\".format(pc_xyz.shape[0]))\n"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Using cKDTree to calculate neighborhood - define functions: "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "metadata": {},
   "outputs": [],
   "source": [
    "def pc_generate_cKDTree(pc_xyz, leafsizei=10):\n",
    "    try:\n",
    "        from scipy import spatial\n",
    "    except ImportError:\n",
    "        raise pc_generate_cKDTree(\"scipy not installed.\")\n",
    "    pc_xyz_cKDTree_tree = spatial.cKDTree(pc_xyz, leafsize=leafsizei)\n",
    "    return pc_xyz_cKDTree_tree\n",
    "\n",
    "def pc_query_cKDTree(pc_xyz_cKDTree_tree, pc_xyz, k=10):\n",
    "    pc_cKDTree_distance, pc_cKDTree_id = pc_xyz_cKDTree_tree.query(pc_xyz, k=k, n_jobs=-1)\n",
    "    return pc_cKDTree_distance, pc_cKDTree_id"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Generate cKDTree and get k=50 closest neighbors. Leaf sizes of ~20 have been shown to work well with lidar point clouds (cf. [https://github.com/UP-RS-ESP/LidarPC-KDTree](https://github.com/UP-RS-ESP/LidarPC-KDTree))."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "metadata": {},
   "outputs": [],
   "source": [
    "pc_xyz_cKDTree_tree = pc_generate_cKDTree(pc_xyz, leafsizei=20)\n",
    "pc_cKDTree_distance, pc_cKDTree_id = pc_query_cKDTree(pc_xyz_cKDTree_tree, pc_xyz, k=50)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 14,
   "metadata": {},
   "outputs": [],
   "source": [
    "#remove first value - because this is the value itself\n",
    "pc_cKDTree_distance = pc_cKDTree_distance[:,1::]\n",
    "pc_cKDTree_id = pc_cKDTree_id[:,1::]"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Note that the returned distances are sorted from the closest (lowest distance) to the highest."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 15,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([[0.01855218, 0.34975934, 0.51496539, ..., 1.87872836, 1.88756394,\n",
       "        1.88840204],\n",
       "       [0.09843266, 0.31375358, 0.47193731, ..., 1.59878976, 1.63499737,\n",
       "        1.65342474],\n",
       "       [0.26868094, 0.46874417, 0.47731256, ..., 1.40681303, 1.40791989,\n",
       "        1.41951103],\n",
       "       ...,\n",
       "       [0.02538329, 0.37629983, 0.4241835 , ..., 1.55242485, 1.55772706,\n",
       "        1.56655723],\n",
       "       [0.02537728, 0.27101715, 0.30805861, ..., 1.40409916, 1.42656202,\n",
       "        1.42753189],\n",
       "       [0.01855218, 0.26982688, 0.3125    , ..., 1.35323786, 1.35952624,\n",
       "        1.39216629]])"
      ]
     },
     "execution_count": 15,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "pc_cKDTree_distance"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Define a simple sphere density function. This takes the sorted distance data, uses the last entry (point farthest away) to calculate a sphere. The number of points in the neighborhood (k) is devided by that sphere volume."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 18,
   "metadata": {},
   "outputs": [],
   "source": [
    "def pc_density_sphere(d, k=10):\n",
    "    dens =  k / np.pi / d[:, -1]**2\n",
    "    return dens"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 21,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Time: 0.03 s\n"
     ]
    }
   ],
   "source": [
    "#pc_cKDTree_density_sphere = pc_density_sphere(pc_cKDTree_distance, k=50)\n",
    "wrapped = wrapper(pc_density_sphere, pc_cKDTree_distance, 50)\n",
    "pc_cKDTree_density_sphere_time = timeit.timeit(wrapped, number=3)\n",
    "print('Average Time: %3.2f s'%(pc_cKDTree_density_sphere_time/3))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 35,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "[4.46304444 5.82171937 7.89846298 ... 6.4852631  7.80995435 8.2117913 ]\n"
     ]
    }
   ],
   "source": [
    "pc_cKDTree_density_sphere = pc_density_sphere(pc_cKDTree_distance, k=50)\n",
    "print(pc_cKDTree_density_sphere)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 42,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Text(0.5, 1.0, 'Scatter Plot X-Y and Sphere Density of Point Cloud for k=50')"
      ]
     },
     "execution_count": 42,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 2 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "plt.scatter(pc_xyz[::10,0], pc_xyz[::10,1], c=pc_cKDTree_density_sphere[::10], s=1, \n",
    "            marker='o', vmin=np.percentile(pc_cKDTree_density_sphere, 2), \n",
    "            vmax=np.percentile(pc_cKDTree_density_sphere, 98) )\n",
    "plt.colorbar()\n",
    "plt.title('Scatter Plot X-Y and Sphere Density of Point Cloud for k=50')"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Define a density function that calculates the density of an ellipsoid. This is computative more intensive, because the x, y, z extent for each point need to be calculated"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 29,
   "metadata": {},
   "outputs": [],
   "source": [
    "def pc_density_ellipsoid(pc_xyz, pc_ids, k=10):\n",
    "    #estimate density in x, y, z directions using an ellipsoid and returning lengthening ratios in x, y, z directions\n",
    "\n",
    "    x = np.max(pc_xyz[pc_ids,0],axis=1) - np.min(pc_xyz[pc_ids,0],axis=1)\n",
    "    y = np.max(pc_xyz[pc_ids,1],axis=1) - np.min(pc_xyz[pc_ids,1],axis=1)\n",
    "    z = np.max(pc_xyz[pc_ids,2],axis=1) - np.min(pc_xyz[pc_ids,2],axis=1)\n",
    "    dens_ellipsoid = k / (4/3 * np.pi * x * y * z )\n",
    "    pts_x_y_ratio = x / y #values < 1 indicate X axis is compressed (and Y axis is longer)\n",
    "    pts_x_z_ratio = x / z #values < 1 indicate X axis is compressed (and Z axis is longer), values > 1 indicate X axis is longer\n",
    "    pts_y_z_ratio = y / z #values < 1 indicate X axis is compressed (and Z axis is longer), values > 1 indicate X axis is longer\n",
    "    return dens_ellipsoid, pts_x_y_ratio, pts_x_z_ratio, pts_y_z_ratio\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 51,
   "metadata": {},
   "outputs": [],
   "source": [
    "pc_cKDTree_density_ellipsoid, pc_cKDTree_density_ellipsoid_x_y, pc_cKDTree_density_ellipsoid_x_z, pc_cKDTree_density_ellipsoid_y_z = pc_density_ellipsoid(pc_xyz, pc_cKDTree_id, k=50)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 52,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Time: 4.94 s\n"
     ]
    }
   ],
   "source": [
    "wrapped = wrapper(pc_density_ellipsoid, pc_cKDTree_distance, pc_cKDTree_id, 50)\n",
    "pc_cKDTree_density_ellipsoid_time = timeit.timeit(wrapped, number=3)\n",
    "print('Average Time: %3.2f s'%(pc_cKDTree_density_ellipsoid_time/3))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 53,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([2.4706473, 2.0076213, 2.5603588, ..., 1.3098061, 1.4258043,\n",
       "       1.7411842], dtype=float32)"
      ]
     },
     "execution_count": 53,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "pc_cKDTree_density_ellipsoid"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 54,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Text(0.5, 1.0, 'Ellipsoid Density')"
      ]
     },
     "execution_count": 54,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 4 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "plt.subplot(1, 2, 1)\n",
    "plt.scatter(pc_xyz[::10,0], pc_xyz[::10,1], c=pc_cKDTree_density_sphere[::10], s=1, \n",
    "            marker='o', vmin=np.percentile(pc_cKDTree_density_sphere, 2), \n",
    "            vmax=np.percentile(pc_cKDTree_density_sphere, 98) )\n",
    "plt.colorbar()\n",
    "plt.title('Sphere Density')\n",
    "\n",
    "plt.subplot(1, 2, 2)\n",
    "plt.scatter(pc_xyz[::10,0], pc_xyz[::10,1], c=pc_cKDTree_density_ellipsoid[::10], s=1, \n",
    "            marker='o', vmin=np.percentile(pc_cKDTree_density_ellipsoid, 2), \n",
    "            vmax=np.percentile(pc_cKDTree_density_ellipsoid, 98) )\n",
    "plt.colorbar()\n",
    "plt.title('Ellipsoid Density')\n"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# CUDA Processing\n",
    "Bring Point Data into CUDA Dataframe. First the X, Y, Z point data and then the ids from the cKDTree run"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 47,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "n_samples = 3348668, n_dims = 3\n",
      "                  X       Y          Z\n",
      "0       -591.265625 -199.00 -73.648056\n",
      "1       -590.421875 -197.75 -73.398056\n",
      "2       -589.812500 -199.00 -73.178055\n",
      "3       -589.906250 -198.50 -73.138062\n",
      "4       -590.578125 -196.75 -73.448059\n",
      "...             ...     ...        ...\n",
      "3348663  550.359375  671.50 -24.208061\n",
      "3348664  551.390625  668.25 -23.178062\n",
      "3348665  554.437500  671.50 -23.128059\n",
      "3348666  550.640625  672.50 -24.338058\n",
      "3348667  550.468750  669.75 -23.808060\n",
      "\n",
      "[3348668 rows x 3 columns]\n",
      "n_ids = 3348668, n_dims = 49\n"
     ]
    }
   ],
   "source": [
    "gdf_float = cudf.DataFrame()\n",
    "gdf_float['X'] = np.ascontiguousarray(pc_xyz[:,0])\n",
    "gdf_float['Y'] = np.ascontiguousarray(pc_xyz[:,1])\n",
    "gdf_float['Z'] = np.ascontiguousarray(pc_xyz[:,2])\n",
    "\n",
    "print('n_samples = %d, n_dims = %d'%(gdf_float.shape[0], gdf_float.shape[1]))\n",
    "print(gdf_float)\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 49,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "n_ids = 3348668, n_dims = 49\n",
      "n_ids = 3348668, n_dims = 49\n"
     ]
    }
   ],
   "source": [
    "pcf_ids = cudf.DataFrame()\n",
    "pcf_ids = np.ascontiguousarray(pc_cKDTree_id)\n",
    "print('n_ids = %d, n_dims = %d'%(pcf_ids.shape[0], pcf_ids.shape[1]))\n",
    "\n",
    "#pcf_d = cudf.DataFrame()\n",
    "#pcf_d = np.ascontiguousarray(pc_cKDTree_distance)\n",
    "#print('n_ids = %d, n_dims = %d'%(pcf_d.shape[0], pcf_d.shape[1]))\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "code",
   "execution_count": 50,
   "metadata": {},
   "outputs": [],
   "source": [
    "def pc_density_ellipsoid_CUDA(gdf_float, pcf_ids, k=10):\n",
    "    #estimate density in x, y, z directions using an ellipsoid and returning lengthening ratios in x, y, z directions\n",
    "\n",
    "    x = np.max(gdf_float['X'][pcf_ids],axis=1) - np.min(gdf_float[pcf_ids,0],axis=1)\n",
    "    y = np.max(gdf_float[pcf_ids,1],axis=1) - np.min(gdf_float[pcf_ids,1],axis=1)\n",
    "    z = np.max(gdf_float[pcf_ids,2],axis=1) - np.min(gdf_float[pcf_ids,2],axis=1)\n",
    "    dens_ellipsoid = k / (4/3 * np.pi * x * y * z )\n",
    "    pts_x_y_ratio = x / y #values < 1 indicate X axis is compressed (and Y axis is longer)\n",
    "    pts_x_z_ratio = x / z #values < 1 indicate X axis is compressed (and Z axis is longer), values > 1 indicate X axis is longer\n",
    "    pts_y_z_ratio = y / z #values < 1 indicate X axis is compressed (and Z axis is longer), values > 1 indicate X axis is longer\n",
    "    return dens_ellipsoid, pts_x_y_ratio, pts_x_z_ratio, pts_y_z_ratio\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 63,
   "metadata": {},
   "outputs": [
    {
     "ename": "ValueError",
     "evalue": "Data must be 1-dimensional",
     "output_type": "error",
     "traceback": [
      "\u001b[0;31m---------------------------------------------------------------------------\u001b[0m",
      "\u001b[0;31mValueError\u001b[0m                                Traceback (most recent call last)",
      "\u001b[0;32m<ipython-input-63-cdb9c12d5f7e>\u001b[0m in \u001b[0;36m<module>\u001b[0;34m\u001b[0m\n\u001b[0;32m----> 1\u001b[0;31m \u001b[0mgdf_float\u001b[0m\u001b[0;34m[\u001b[0m\u001b[0;34m'X'\u001b[0m\u001b[0;34m]\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0miloc\u001b[0m\u001b[0;34m[\u001b[0m\u001b[0mpcf_ids\u001b[0m\u001b[0;34m]\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m",
      "\u001b[0;32m~/miniconda3/envs/PC_py38/lib/python3.8/site-packages/cudf/core/indexing.py\u001b[0m in \u001b[0;36m__getitem__\u001b[0;34m(self, arg)\u001b[0m\n\u001b[1;32m     72\u001b[0m         \u001b[0;32mif\u001b[0m \u001b[0misinstance\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0marg\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mtuple\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m     73\u001b[0m             \u001b[0marg\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mlist\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0marg\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m---> 74\u001b[0;31m         \u001b[0mdata\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0m_sr\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0m_column\u001b[0m\u001b[0;34m[\u001b[0m\u001b[0marg\u001b[0m\u001b[0;34m]\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m     75\u001b[0m         \u001b[0;32mif\u001b[0m \u001b[0mis_scalar\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mdata\u001b[0m\u001b[0;34m)\u001b[0m \u001b[0;32mor\u001b[0m \u001b[0mdata\u001b[0m \u001b[0;32mis\u001b[0m \u001b[0;32mNone\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m     76\u001b[0m             \u001b[0;32mreturn\u001b[0m \u001b[0mdata\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
      "\u001b[0;32m~/miniconda3/envs/PC_py38/lib/python3.8/site-packages/cudf/core/column/column.py\u001b[0m in \u001b[0;36m__getitem__\u001b[0;34m(self, arg)\u001b[0m\n\u001b[1;32m    622\u001b[0m                 \u001b[0;32mreturn\u001b[0m \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mtake\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mgather_map\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m    623\u001b[0m         \u001b[0;32melse\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m--> 624\u001b[0;31m             \u001b[0marg\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mas_column\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0marg\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m    625\u001b[0m             \u001b[0;32mif\u001b[0m \u001b[0mlen\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0marg\u001b[0m\u001b[0;34m)\u001b[0m \u001b[0;34m==\u001b[0m \u001b[0;36m0\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m    626\u001b[0m                 \u001b[0marg\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mas_column\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m[\u001b[0m\u001b[0;34m]\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mdtype\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0;34m\"int32\"\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
      "\u001b[0;32m~/miniconda3/envs/PC_py38/lib/python3.8/site-packages/cudf/core/column/column.py\u001b[0m in \u001b[0;36mas_column\u001b[0;34m(arbitrary, nan_as_null, dtype, length)\u001b[0m\n\u001b[1;32m   1647\u001b[0m         \u001b[0;31m# CUDF assumes values are always contiguous\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m   1648\u001b[0m         \u001b[0;32mif\u001b[0m \u001b[0mlen\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mshape\u001b[0m\u001b[0;34m)\u001b[0m \u001b[0;34m>\u001b[0m \u001b[0;36m1\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m-> 1649\u001b[0;31m             \u001b[0;32mraise\u001b[0m \u001b[0mValueError\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m\"Data must be 1-dimensional\"\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m   1650\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m   1651\u001b[0m         \u001b[0marbitrary\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mnp\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0masarray\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0marbitrary\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
      "\u001b[0;31mValueError\u001b[0m: Data must be 1-dimensional"
     ]
    }
   ],
   "source": [
    "gdf_float['X'].iloc[pcf_ids]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 61,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([[-591.25   , -591.03125, -591.1719 , ..., -589.7344 , -590.625  ,\n",
       "        -590.6094 ],\n",
       "       [-590.3281 , -590.59375, -590.0469 , ..., -588.9531 , -588.90625,\n",
       "        -589.75   ],\n",
       "       [-589.71875, -589.3594 , -590.28125, ..., -588.6719 , -590.4219 ,\n",
       "        -588.65625],\n",
       "       ...,\n",
       "       [ 554.4531 ,  554.71875,  554.2031 , ...,  555.5625 ,  553.59375,\n",
       "         553.15625],\n",
       "       [ 550.65625,  550.8594 ,  550.8906 , ...,  551.625  ,  549.40625,\n",
       "         549.4219 ],\n",
       "       [ 550.4531 ,  550.40625,  550.28125, ...,  551.25   ,  549.3125 ,\n",
       "         549.34375]], dtype=float32)"
      ]
     },
     "execution_count": 61,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "pc_xyz[pc_ids,0]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 55,
   "metadata": {},
   "outputs": [
    {
     "ename": "TypeError",
     "evalue": "unhashable type: 'numpy.ndarray'",
     "output_type": "error",
     "traceback": [
      "\u001b[0;31m---------------------------------------------------------------------------\u001b[0m",
      "\u001b[0;31mTypeError\u001b[0m                                 Traceback (most recent call last)",
      "\u001b[0;32m<ipython-input-55-9a17329a6ce9>\u001b[0m in \u001b[0;36m<module>\u001b[0;34m\u001b[0m\n\u001b[0;32m----> 1\u001b[0;31m \u001b[0mpcf_density_ellipsoid\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mpcf_density_ellipsoid_x_y\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mpcf_density_ellipsoid_x_z\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mpcf_density_ellipsoid_y_z\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mpc_density_ellipsoid\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mgdf_float\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mpcf_ids\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mk\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0;36m50\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m",
      "\u001b[0;32m<ipython-input-29-136654e50fa4>\u001b[0m in \u001b[0;36mpc_density_ellipsoid\u001b[0;34m(pc_xyz, pc_ids, k)\u001b[0m\n\u001b[1;32m      2\u001b[0m     \u001b[0;31m#estimate density in x, y, z directions using an ellipsoid and returning lengthening ratios in x, y, z directions\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m      3\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m----> 4\u001b[0;31m     \u001b[0mx\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mnp\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mmax\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mpc_xyz\u001b[0m\u001b[0;34m[\u001b[0m\u001b[0mpc_ids\u001b[0m\u001b[0;34m,\u001b[0m\u001b[0;36m0\u001b[0m\u001b[0;34m]\u001b[0m\u001b[0;34m,\u001b[0m\u001b[0maxis\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0;36m1\u001b[0m\u001b[0;34m)\u001b[0m \u001b[0;34m-\u001b[0m \u001b[0mnp\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mmin\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mpc_xyz\u001b[0m\u001b[0;34m[\u001b[0m\u001b[0mpc_ids\u001b[0m\u001b[0;34m,\u001b[0m\u001b[0;36m0\u001b[0m\u001b[0;34m]\u001b[0m\u001b[0;34m,\u001b[0m\u001b[0maxis\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0;36m1\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m      5\u001b[0m     \u001b[0my\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mnp\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mmax\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mpc_xyz\u001b[0m\u001b[0;34m[\u001b[0m\u001b[0mpc_ids\u001b[0m\u001b[0;34m,\u001b[0m\u001b[0;36m1\u001b[0m\u001b[0;34m]\u001b[0m\u001b[0;34m,\u001b[0m\u001b[0maxis\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0;36m1\u001b[0m\u001b[0;34m)\u001b[0m \u001b[0;34m-\u001b[0m \u001b[0mnp\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mmin\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mpc_xyz\u001b[0m\u001b[0;34m[\u001b[0m\u001b[0mpc_ids\u001b[0m\u001b[0;34m,\u001b[0m\u001b[0;36m1\u001b[0m\u001b[0;34m]\u001b[0m\u001b[0;34m,\u001b[0m\u001b[0maxis\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0;36m1\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m      6\u001b[0m     \u001b[0mz\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mnp\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mmax\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mpc_xyz\u001b[0m\u001b[0;34m[\u001b[0m\u001b[0mpc_ids\u001b[0m\u001b[0;34m,\u001b[0m\u001b[0;36m2\u001b[0m\u001b[0;34m]\u001b[0m\u001b[0;34m,\u001b[0m\u001b[0maxis\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0;36m1\u001b[0m\u001b[0;34m)\u001b[0m \u001b[0;34m-\u001b[0m \u001b[0mnp\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mmin\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mpc_xyz\u001b[0m\u001b[0;34m[\u001b[0m\u001b[0mpc_ids\u001b[0m\u001b[0;34m,\u001b[0m\u001b[0;36m2\u001b[0m\u001b[0;34m]\u001b[0m\u001b[0;34m,\u001b[0m\u001b[0maxis\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0;36m1\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
      "\u001b[0;32m~/miniconda3/envs/PC_py38/lib/python3.8/contextlib.py\u001b[0m in \u001b[0;36minner\u001b[0;34m(*args, **kwds)\u001b[0m\n\u001b[1;32m     73\u001b[0m         \u001b[0;32mdef\u001b[0m \u001b[0minner\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m*\u001b[0m\u001b[0margs\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0;34m**\u001b[0m\u001b[0mkwds\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m     74\u001b[0m             \u001b[0;32mwith\u001b[0m \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0m_recreate_cm\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m---> 75\u001b[0;31m                 \u001b[0;32mreturn\u001b[0m \u001b[0mfunc\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m*\u001b[0m\u001b[0margs\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0;34m**\u001b[0m\u001b[0mkwds\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m     76\u001b[0m         \u001b[0;32mreturn\u001b[0m \u001b[0minner\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m     77\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n",
      "\u001b[0;32m~/miniconda3/envs/PC_py38/lib/python3.8/site-packages/cudf/core/dataframe.py\u001b[0m in \u001b[0;36m__getitem__\u001b[0;34m(self, arg)\u001b[0m\n\u001b[1;32m    651\u001b[0m         \"\"\"\n\u001b[1;32m    652\u001b[0m         \u001b[0;32mif\u001b[0m \u001b[0mis_scalar\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0marg\u001b[0m\u001b[0;34m)\u001b[0m \u001b[0;32mor\u001b[0m \u001b[0misinstance\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0marg\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mtuple\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m--> 653\u001b[0;31m             \u001b[0;32mreturn\u001b[0m \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0m_get_columns_by_label\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0marg\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mdowncast\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0;32mTrue\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m    654\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m    655\u001b[0m         \u001b[0;32melif\u001b[0m \u001b[0misinstance\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0marg\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mslice\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
      "\u001b[0;32m~/miniconda3/envs/PC_py38/lib/python3.8/site-packages/cudf/core/frame.py\u001b[0m in \u001b[0;36m_get_columns_by_label\u001b[0;34m(self, labels, downcast)\u001b[0m\n\u001b[1;32m    441\u001b[0m         \u001b[0mIf\u001b[0m \u001b[0mdowncast\u001b[0m \u001b[0;32mis\u001b[0m \u001b[0;32mTrue\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0;32mtry\u001b[0m \u001b[0;32mand\u001b[0m \u001b[0mdowncast\u001b[0m \u001b[0;32mfrom\u001b[0m \u001b[0ma\u001b[0m \u001b[0mDataFrame\u001b[0m \u001b[0mto\u001b[0m \u001b[0ma\u001b[0m \u001b[0mSeries\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m    442\u001b[0m         \"\"\"\n\u001b[0;32m--> 443\u001b[0;31m         \u001b[0mnew_data\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0m_data\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mselect_by_label\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mlabels\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m    444\u001b[0m         \u001b[0;32mif\u001b[0m \u001b[0mdowncast\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m    445\u001b[0m             \u001b[0;32mif\u001b[0m \u001b[0mis_scalar\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mlabels\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
      "\u001b[0;32m~/miniconda3/envs/PC_py38/lib/python3.8/site-packages/cudf/core/column_accessor.py\u001b[0m in \u001b[0;36mselect_by_label\u001b[0;34m(self, key)\u001b[0m\n\u001b[1;32m    217\u001b[0m                 \u001b[0;32mif\u001b[0m \u001b[0many\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0misinstance\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mk\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mslice\u001b[0m\u001b[0;34m)\u001b[0m \u001b[0;32mfor\u001b[0m \u001b[0mk\u001b[0m \u001b[0;32min\u001b[0m \u001b[0mkey\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m    218\u001b[0m                     \u001b[0;32mreturn\u001b[0m \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0m_select_by_label_with_wildcard\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mkey\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m--> 219\u001b[0;31m             \u001b[0;32mreturn\u001b[0m \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0m_select_by_label_grouped\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mkey\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m    220\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m    221\u001b[0m     \u001b[0;32mdef\u001b[0m \u001b[0mselect_by_index\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mself\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mindex\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
      "\u001b[0;32m~/miniconda3/envs/PC_py38/lib/python3.8/site-packages/cudf/core/column_accessor.py\u001b[0m in \u001b[0;36m_select_by_label_grouped\u001b[0;34m(self, key)\u001b[0m\n\u001b[1;32m    265\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m    266\u001b[0m     \u001b[0;32mdef\u001b[0m \u001b[0m_select_by_label_grouped\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mself\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mkey\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m--> 267\u001b[0;31m         \u001b[0mresult\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0m_grouped_data\u001b[0m\u001b[0;34m[\u001b[0m\u001b[0mkey\u001b[0m\u001b[0;34m]\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m    268\u001b[0m         \u001b[0;32mif\u001b[0m \u001b[0misinstance\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mresult\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mcudf\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mcore\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mcolumn\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mColumnBase\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m    269\u001b[0m             \u001b[0;32mreturn\u001b[0m \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0m__class__\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m{\u001b[0m\u001b[0mkey\u001b[0m\u001b[0;34m:\u001b[0m \u001b[0mresult\u001b[0m\u001b[0;34m}\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
      "\u001b[0;31mTypeError\u001b[0m: unhashable type: 'numpy.ndarray'"
     ]
    }
   ],
   "source": [
    "pcf_density_ellipsoid, pcf_density_ellipsoid_x_y, pcf_density_ellipsoid_x_z, pcf_density_ellipsoid_y_z = pc_density_ellipsoid(gdf_float, pcf_ids, k=50)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  }
 ],
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